AccScience Publishing / AIH / Online First / DOI: 10.36922/aih.8195
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ORIGINAL RESEARCH ARTICLE

Leveraging the smarts in your phone: An artificial intelligence-driven iOS application for neurosurgical navigation of external ventricular drains

Andrew Abumoussa1 ,  Benjamin Succop2* ,  Carolyn Quinsey3 ,  Yueh Lee4 ,  Sivakumar Jaikumar1
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1 Department of Neurosurgery, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
2 Department of Neurosurgery, School of Medicine, Duke University, Durham, North Carolina, United States of America
3 Department of Neurosurgery, School of Medicine, University of Missouri, Columbia, Missouri, United States of America
4 Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
AIH 2025, 2(4), 129–138; https://doi.org/10.36922/aih.8195
Received: 25 December 2024 | Revised: 27 July 2025 | Accepted: 31 August 2025 | Published online: 23 September 2025
(This article belongs to the Special Issue Sensors and circuits for AI in health)
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

External ventricular drain (EVD) placement is a critical neurosurgical procedure traditionally performed freehand, with inherent risks of malposition, infection, and hemorrhage. Recent advances in artificial intelligence (AI), particularly in medical imaging and real-time computer vision, have enabled the development of portable navigation tools that may enhance accuracy, safety, and bedside accessibility. This study evaluated whether iOS devices equipped with a TrueDepth camera could perform real-time object and facial recognition, tracking, and semantic segmentation of computed tomography (CT) scans for non-immobilized heads to guide EVD placement via a custom AI-driven application. A custom iOS application was developed to provide a complete, real-time surgical navigation experience on an iPhone or iPad Pro. Three AI models were trained, tuned, and validated: a semantic segmentation model for brain anatomy, a semantic segmentation model for facial features, and an object detection model for a custom EVD stylet attachment. GPU programming accelerated on-device real-time, continuous registration while optimizing power consumption. A UNet convolutional neural network trained on eight 1 mm head CTs achieved 98.3% testing and 98.2% validation accuracy using a 50/50 test–validation split, segmenting a thin-cut CT in 3 s on an iPhone 12 Pro. Point cloud merging of patient anatomy took 4 seconds with an initial depth scan of 30,000 points, updating in real time with a cumulative error of 1 × 10-8 cm. Transfer learning-powered EVD tracking, trained for 1,000 epochs, achieved an intersection over union of 1.0 and 0.98 for the detection model, with inference times of 800 μs on Apple’s Neural Engine. This feasibility study demonstrates that iOS devices with TrueDepth cameras can enable real-time, continuous surgical navigation for EVD stylets.

Keywords
External ventricular drain
Surgical navigation
Artificial intelligence
Machine learning
Funding
This work was graciously funded by UNC Health’s Innovation Pilot Grant (Grant no.: 29201).
Conflict of interest
The authors declare they have no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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